A new algorithm is proposed for removing large objects from digital images.
The challenge is to fill in the hole that is left behind in a visually plausible
way. In the past, this problem has been addressed by two classes of algorithms:
1) texture synthesis algorithms for generating large image regions from sample
textures and 2) inpainting techniques for filling in small image gaps. The former
has been demonstrated for textures repeating twodimensional patterns with some
stochasticity; the latter focus on linear "structures" which can be thought of as
one-dimensional patterns, such as lines and object contours. This paper presents
a novel and efficient algorithm that combines the advantages of these two
approaches. We first note that exemplar-based texture synthesis contains the
essential process required to replicate both texture and structure; the success
of structure propagation, however, is highly dependent on the order in which the
filling proceeds. We propose a best-first algorithm in which the confidence in
the synthesized pixel values is propagated in a manner similar to the propagation
of information in inpainting. The actual color values are computed using
exemplar-based synthesis. In this paper, the simultaneous propagation of texture
and structure information is achieved by a single, efficient algorithm.
Computational efficiency is achieved by a block-based sampling process. A number
of examples on real and synthetic images demonstrate the effectiveness of our
algorithm in removing large occluding objects, as well as thin scratches.
Robustness with respect to the shape of the manually selected target region is
also demonstrated. Our results compare favorably to those obtained by existing
techniques.